In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.

The use of multiple sensors in modern day vehicular applications is necessary to provide a complete outlook of surroundings for advanced driver assistance systems (ADAS) and automated driving. The fusion of these sensors provides increased certainty in the recognition, localization and prediction of surroundings. A deep learning-based sensor fusion system is proposed to fuse two independent, multi-modal sensor sources. This system is shown to successfully learn the complex capabilities of an existing state-of-the-art sensor fusion system and generalize well to new sensor fusion datasets. It has high precision and recall with minimal confusion after training on several million examples of labeled multi-modal sensor data. It is robust, has a sustainable training time, and has real-time response capabilities on a deep learning PC with a single NVIDIA GeForce GTX 980Ti graphical processing unit (GPU).

This book includes papers from the section “Multisensor Information Fusion”, from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technologies, estimation and filtering, and the latest research, artificial intelligence involving deep learning.

This book includes selected papers from the 13th IEEE International Conference on Multisensor Integration and Fusion for Intelligent Systems (MFI 2017) held in Daegu, Korea, November 16–22, 2017. It covers various topics, including sensor/actuator networks, distributed and cloud architectures, bio-inspired systems and evolutionary approaches, methods of cognitive sensor fusion, Bayesian approaches, fuzzy systems and neural networks, biomedical applications, autonomous land, sea and air vehicles, localization, tracking, SLAM, 3D perception, manipulation with multifinger hands, robotics, micro/nano systems, information fusion and sensors, and multimodal integration in HCI and HRI. The book is intended for robotics scientists, data and information fusion scientists, researchers and professionals at universities, research institutes and laboratories.

Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data An exploration of the benefits of neural networks in real-time environmental sensor data analysis Practical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition An analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.

While it may be attractive to view sensors as simple transducers which convert physical quantities into electrical signals, the truth of the matter is more complex. The engineer should have a proper understanding of the physics involved in the conversion process, including interactions with other measurable quantities. A deep understanding of these interactions can be leveraged to apply sensor fusion techniques to minimize noise and/or extract additional information from sensor signals. Advances in microcontroller and MEMS manufacturing, along with improved internet connectivity, have enabled cost-effective wearable and Internet of Things sensor applications. At the same time, machine learning techniques have gone mainstream, so that those same applications can now be more intelligent than ever before. This book explores these topics in the context of a small set of sensor types. We provide some basic understanding of sensor operation for accelerometers, magnetometers, gyroscopes, and pressure sensors. We show how information from these can be fused to provide estimates of orientation. Then we explore the topics of machine learning and sensor data analytics.

The fast development of new technologies related to sensor solutions, cyber-physical-systems, cloud computing, the Internet of Things (IoT) and their applications in the industry has led to a new modern era where the industry itself has faced a new industrial revolution called Industry 4.0. With the help of machine learning techniques, sensory solutions and the application of IoT, Industry 4.0 has been able to achieve fully autonomous and intelligent processes that can communicate with each other and could be located hundreds of miles away. As a consequence, in the presented work, an implementation of the concept mentioned earlier is acquired to create an intelligent reconfigurable measurement system technology that takes multiple outputs from different sensors (pressure sensor, accelerometer, temperature, and light absorption) and performed the data analysis and data acquisition. The methodology used is an advanced analytics framework of machine learning as an end-to-end model with a combination of nonlinear multi-layers for structuring the multi-sensor fusion, this framework uses a deep learning approach, which is an end-to-end learning structure that takes the outputs of the multi-sensor network and performs classification, data linearization and calibration for the different sensors. The multi-sensor data fusion is performed using a centralized architecture (microcontroller and PC), taking an IoT implementation for data transfer. The data alignment and data associations are performed within a desktop PC using a microcontroller as a communication node. Then, a convolutional neural network is used for classifying the data and then pass it to a deep fully connected neural network for its linearization and calibration. The validation of the methodology is performed using 150, 000 data points as reference for the calibration and linearization processes as well as the classification of the data coming from the multi-sensor system. A user-to-system communication framework is designed to perform the multi-sensor fusion and also to enable the user control of the processes. With the communication framework mentioned above, an easy-to-use device has been designed and developed to help to understand the structure of sensor fusion using deep learning as a contribution to the academic learning community. The contributions of the presented work lie in the usage of a deep learning framework for multi-sensor fusion with a centralized low-cost architecture. The main focus is to create a low-cost solution for sensor fusion that relies on the application of an Internet of Things (IoT) and machine learning data structures; this will help to prove how using machine learning methods can contribute to the construction of such measurement system. It is concluded that a multi-sensor fusion approach using deep learning as a framework model gives excellent results compared to benchmark methods for the integration of different sensors, accomplishing at the same time the linearization and calibration of the outputs coming from these sensors.

Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data An exploration of the benefits of neural networks in real-time environmental sensor data analysis Practical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition An analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.

This book on classification in biomedical image applications presents original and valuable research work on advances in this field, which covers the taxonomy of both supervised and unsupervised models, standards, algorithms, applications and challenges. Further, the book highlights recent scientific research on artificial neural networks in biomedical applications, addressing the fundamentals of artificial neural networks, support vector machines and other advanced classifiers, as well as their design and optimization. In addition to exploring recent endeavours in the multidisciplinary domain of sensors, the book introduces readers to basic definitions and features, signal filters and processing, biomedical sensors and automation of biomeasurement systems. The target audience includes researchers and students at engineering and medical schools, researchers and engineers in the biomedical industry, medical doctors and healthcare professionals.